Opening book details…
Can I read Evaluation Measures and Applications for Explainable AI on EtoBox?
Evaluation Measures and Applications for Explainable AI by Mayank Chopra; Ajay Kumar is a book available to read on EtoBox.
What is Evaluation Measures and Applications for Explainable AI about?
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
- Author
- Mayank Chopra; Ajay Kumar
- Publisher
- Springer International Publishing AG
- Published
- 2022
- Language
- EN
- ISBN
- 9783031128066
- Subjects
- Mathematics, Engineering, Philosophy
More by Mayank Chopra; Ajay Kumar
Browse all works by Mayank Chopra; Ajay Kumar
Similar books
- Explainable AI in Health Informatics — Rajanikanth Aluvalu, Mayuri Mehta, Patrick Siarry (2024)
- Explainable Artificial Intelligence for Intelligent Transportation Systems — Adadi, Amina & Bouhoute, Afaf (2023)
- Convergence of Blockchain and Explainable Artificial Intelligence BlockXAI — Akansha Singh & Krishna Kant Singh (2024)
- Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges — Feiyu Xu; Hans Uszkoreit; Yangzhou Du; Wei Fan; Dongyan Zhao; Jun Zhu (2019)
- Explainable Machine Learning for Multimedia Based Healthcare Applications — M. Shamim Hossain & Utku Kose & Deepak Gupta (2023)
- Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications — Amjad Rehman Khan & Tanzila Saba (2024)